Methods and apparatus for predicting airline seat availability

ABSTRACT

Systems predict the availability of travel resources and select an appropriate data source for obtaining travel resource inventory availability information by analyzing historical travel resource availability data. Such systems receive customer travel requests and determine candidate itineraries. The candidate itineraries are used to determine the probability that the candidate itineraries will remain available for certain periods of time based on historical availability information and fare rules. Such systems increase the reliability of booking travel itineraries by determining when it is necessary to obtain updated availability information. The systems make this determination by looking up candidate itineraries in a situation table calculated from historical availability information.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.09/697,645, filed Oct. 27, 2000, the contents of which are incorporatedherein by reference.

DESCRIPTION OF THE INVENTION

1. Field of the Invention

This invention relates generally to methods and systems for predictingavailability of a given inventory resource. In particular, the presentinvention comprises a method and system for predicting the futureavailability of a travel inventory resource given historical inventorydata. The present invention also comprises a method and system forincreasing reliability of booking airline travel itineraries by optimaluse of inventory data sources.

2. Background of the Invention

In the travel industry, customers wishing to purchase tickets for travelgenerally begin by placing a request for a particular travel itinerary,either personally using an on-line or telephone-based reservation systemor through a travel agent or travel provider. The customer's travelrequest may include, for example, proposed dates, departure location,and arrival location. In response, the customer is typically presentedwith a proposed itinerary and quoted the current price for the proposeditinerary. At this point, the customer generally must make a decisionwhether to purchase the ticket immediately. Customers wishing to waitbefore committing to the purchase may opt to place the ticket on hold,which, if the option is available, allows the customer to reserve thespecific itinerary for a short period, typically 24 hours, with noguarantees that the price be the same at time of purchase. In a volatiletravel market, ticket prices for travel, particularly for airlinetravel, are adjusted frequently, often several times in a single day,adding great uncertainty to placing a travel itinerary on hold.

Customers may want to place travel reservations on hold for a variety ofreasons. Customers may need to first verify travel plans and coordinatetravel with another party. Some customers may wish to wait and see if apreferred itinerary becomes available. Customers for whom price is aconcern may want to wait and see if the travel price decreases. Once aticket is purchased, however, the customer must often pay fees to cancelor change the itinerary.

Conventional systems, however, typically allow customers to place anitinerary on hold for only a limited time period, such as 24 hours.Placing an itinerary on hold in the conventional fashion may provide thecustomer with an extra day of flexibility but, after this brief time haslapsed, the customer is once again faced with immediately purchasing theticket or again placing the itinerary on hold. Using conventional travelreservation systems, such as SABRE, EDS', System One, Covia, World Span,and proprietary reservation systems run by airlines, customers are notprovided with information regarding the likelihood that a preferredtravel itinerary will remain available for booking in the future. Travelproviders, travel agents and other booking agents would also like to beable to offer this information to customers that seek advice regardingthis decision about whether to make an immediate purchase or to deferthe decision to purchase.

Travel providers face a related problem. Travel providers seek tomaximize revenue by selling all available seats in the travel resource,such as a plane, train, or bus. To facilitate maximum revenue, travelproviders typically offer seats in the travel resource in various“booking classes,” which vary, for example, in price, availability, andrestrictions. Airlines, for example, offer seats on an airplane withinPassenger Name Record (“PNR”) booking classes, such as “Y,” “Q,” and“L,” to name a few. A ticket in “Y” class, for example, may be a fullfare coach ticket with no restrictions as to when it can be booked orwhether it can be changed. Tickets in “L” class, however, while offeredat a significantly reduced fare price, may require a 14-day advancepurchase. Similarly, tickets in “Q” class might support a fare rule thatrequires only a three-day advance purchase.

Since travel providers, such as airlines, seek to make use of allavailable seats, while maximizing sales of the higher revenue tickets,the providers tend to offer fewer seats in each of the descendingclasses. For instance, if there were three classes “Y,” “Q,” and “L” fora flight on an airplane having 100 seats, an airline might initiallyoffer 100 seats in “Y” class, 50 seats in “Q” class, and 25 seats in “L”class. By doing so, the airline is not offering 175 seats, but ratherindicating a preference for the higher revenue classes, so that astickets are purchased, and the travel resource begins to fill up, theremaining seats will always be in the higher classes. As each ticket issold, the number of available seats in each class is reduced by one toensure that lower revenue tickets are not sold at the expense of thehigher revenue tickets. Continuing with the above example, it would bepossible for all of the 100 “Y” class tickets to be sold, but there willbe a maximum of 50 and 25 sold in the “Q” and “L” classes, respectively.Once the lower revenue classes are sold, those persons needing to travelwill have to purchase the higher-revenue tickets. Similarly, on thoseflights that are not full, the opportunity to purchase lower pricedtickets will ideally maximize ticket sales because of the discountedprices.

Electronic reservation systems, such as SABRE, allow travel providers,travel agents, and other booking services to all book and reserve seatsessentially simultaneously. While seats are being booked and reserved bymultiple services essentially simultaneously, it becomes a challenge tomaintain accurate records of the number of remaining seats in eachclass. A particular airline will presumably have accurate records ofseat availability for its own flights, but depending on how a centralreservation service obtains its seat availability information, thecentral reservation service's information may not be perfectlysynchronized with actual seat availability.

In the airline industry, central reservation systems employ at least twodifferent models for updating the inventory of available seats. Using anAvailability Status model (“AVS”), a central reservation systemperiodically accesses the host computers of travel providers, checksinventory for all travel resources, and stores the information locally.When the central reservation system receives a request to checkavailability for a certain travel resource, the system simply returns aresult based on the local information that has been stored since themost recent check. Since a reservation system typically incurs chargeseach time it accesses a database, using AVS is cost efficient because,in the AVS system, remote databases with associated access charges areaccessed only periodically. Using AVS may be unreliable, however,because a certain travel resource may sell out between system updates,thereby causing overbooking, or a seat may become available unbeknownstto the reservation system, thereby resulting in a lost sale.

By contrast, central reservation systems using the method of DirectConnect Availability (“DCA”) do not suffer from this potentialunreliability, however they are significantly more expensive. Thesesystems do not store information locally, but rather connect directly toan airline's seat availability database with every customer request.Therefore, central reservation systems using DCA are potentially moreaccurate than systems using AVS because the seat availabilityinformation is always current. Using DCA, however, is more expensivethan AVS because the DCA system accesses databases more frequently thanAVS and therefore generates more access fees than AVS.

Thus, the ideal balance between cost and benefit would be to use AVSexcept when it would be sufficiently likely to create a lost a sale oran over booking. The problem is that there is currently no availabletechnology to determine when it is safe to use AVS and when it issufficiently important to use DCA.

SUMMARY OF THE INVENTION

In accordance with the invention, methods and systems consistent withthe present invention overcome the shortcomings of existing systems bypredicting travel resource availability. Such methods and systems firstobtain a candidate itinerary. The method then determines a probabilitythat the candidate itinerary will remain available for booking for aperiod of time, and then outputs the probability. The method may obtainthe candidate itinerary by receiving a customer request for travel, andselecting from a list of flights a candidate itinerary that satisfiesthe customer request.

The method may determine the probability by calculating the probabilitythat the client itinerary will be available based upon historicalavailability information. The method may determine when the candidateitinerary will become unavailable for booking based on fare rules. Themethod may output a probability by predicting when the itinerary willbecome unavailable after a lower-priced itinerary has becomeunavailable. The method may calculate the probability by predicting whenthe itinerary will close in relation to a flight departure date. Themethod may also calculate the likelihood that an unavailable itinerarywill become available again.

A method consistent with the present invention increases the reliabilityof booking airline travel itineraries by obtaining a candidate itineraryincluding availability information and determining whether theavailability information should be updated based on the candidateitinerary.

A method consistent with the present invention may select anavailability source by obtaining availability information from at leasttwo sources, determining differences between the availabilityinformation from the sources, and discarding availability informationrendered irrelevant by fare rules.

BRIEF DESCRIPTION OF THE DRAWING

For a more complete understanding of the present invention and itsadvantages, reference is made to the following description inconjunction with the following drawings in which:

FIG. 1 shows the steps of predicting the availability of a travelitinerary consistent with the present invention.

FIG. 2 illustrates an exemplary output of a method consistent with thepresent invention.

FIG. 3 shows the steps of a method for generating probability andsituation tables based upon past availability information consistentwith the present invention.

FIG. 4 is a flow diagram illustrating the steps of a method forincreasing reliability of booking airline travel itineraries.

DETAILED DESCRIPTION

The present invention now will be described more fully hereinafter withreference to the accompanying drawings, in which some, but not allembodiments of the invention are shown. Indeed, these inventions may beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein; rather, these embodiments areprovided so that this disclosure will satisfy applicable legalrequirements. Like numbers refer to like elements throughout.

Reference will now be made in detail to an implementation consistentwith the principles of the present invention as illustrated in theaccompanying drawings. Wherever possible, the same reference numberswill be used throughout the drawings and the following description torefer to the same or like parts.

FIG. 1 shows the steps of predicting the availability of a travelitinerary consistent with the present invention. The process ofpredicting availability is initiated by obtaining a customer request(step 100). This request can be obtained via several channels includingtalking to a travel agent, accessing an on-line reservation system suchas Sabre or American Airlines, or via other modes of correspondence. Acustomer request includes, for example, the desired departure location,arrival location, and number of passengers. A customer request mayoptionally include a desired date or date ranges, flight times, PNRbooking class, airlines, airports or price range.

In step 105, one or more candidate itineraries are generated based onthe customer request. A candidate itinerary is a specific flight thatmeets the customer request requirements, such as, for example, aspecific airline and flight number on a specific date. A flight numberrepresents a distinct departure time and arrival time at distinctairports, and may or may not include stops.

Next, for each candidate itinerary, availability information iscollected from conventional global distribution systems (GDS) such asSabre, World Span, Amadeus and Galileo (step 110). For each flight, theGDS typically provides the number of seats that are currently availablefor booking within each fare class. Next, each candidate itinerary iscompared to the number of currently available seats within each class todetermine whether there are adequate seats in any of the booking classesto satisfy the customer request (step 120), i.e., whether the candidateitinerary is available.

If the availability information indicates that there are sufficientcurrently available seats to book the candidate itinerary, theprobability that the candidate itinerary will continue to be availablefor the number of passengers requested by the client is determined (step130). In one embodiment consistent with the present invention, theprobability that a candidate itinerary will remain open is predicated bylooking up the candidate itinerary in a probability table thatrepresents a historical percentage of instances that the flight in thecandidate itinerary remained open for certain periods of time. Thus, instep 130, the candidate itinerary may be compared to entries in theprobability table to determine the duration and probability thecandidate itinerary will remain open. Based upon this comparison step,the number of days and percentage probability that the candidateitinerary will remain available may be determined.

A probability table consistent with the present invention may begenerated by computing the percentage of times the flight remained openunder other similar situations. For example, if a candidate itinerarycomprises an airline and flight number, a probability table consistentwith the present invention may contain, for example, percentagesrepresenting the number of times that the same airline and flight numberin previous situations closed one week before departure, three daysbefore departure, and one day before departure.

If the availability information indicates that there are not sufficientcurrently available seats to book the candidate itinerary, theprobability that the candidate itinerary will become available for thenumber of passengers requested by the client may be determined (step125). In one embodiment consistent with the present invention, theprobability that a candidate itinerary will become available may bedetermined by accessing a probability table that represents a historicalpercentage of instances that the desired number of seats in therequested booking class for the flight in the candidate itinerary becameavailable after being closed. As such, a probability table consistentwith the present invention may indicate, for example, the likelihoodthat a currently-closed booking class will reopen. In the airlineindustry, it is quite common to adjust the inventory of available seatsbased on booking demand. If a currently closed booking class is on aflight that still has many seats remaining, for example, the airline mayreopen the booking class by adding available seats to the inventory ofthat booking class. By analyzing the past trends of reopening bookingclasses based on the current state of availability, it is possible toreasonably predict whether the class will reopen within a certain periodof time such as, for example, the next 7 days. In other embodiments, thecandidate itinerary may be discarded if the availability informationindicates that there are not sufficient currently available seats tobook the candidate itinerary.

The process described above is repeated for each candidate itinerary.Finally, the availability and probability information is provided to thecustomer in step 140.

FIG. 2 is an example of the output to the customer. Section 200 shows acandidate itinerary and section 210 indicates the cost of the candidateitinerary. Section 220 indicates the number of days and percentageprobability that the candidate itinerary in section 200 will remainavailable for booking.

FIG. 3 shows the steps of a method for generating probability andsituation tables based upon past availability information consistentwith the present invention. In one embodiment of the present invention,a situation table contains candidate itinerary parameters (e.g. airline,flight number, number of passengers and day of week) for which ananalysis of historical availability information has indicated currentavailability information must be obtained prior to booking. For example,in step 300, flight availability information is collected for multiplecandidate itineraries. For each candidate itinerary, the system queriesthe probability table in the database. In step 310, if a probabilityrecord is found with matching candidate itinerary parameters, theprobability is updated based upon the currently collected availabilityinformation (step 320). If a record is not found in the probabilitytable, a new probability record is created (step 315).

In step 330, the method compares the AVS and DCA data for the candidateitinerary to determine differences in availability information. For eachbooking class, the method compares the AVS data for that class with thecorresponding DCA data. For illustrative purposes only, the method isexplained with reference to the availability data in Example 1 shownbelow. Example 1 shows AVS and DCA availability information for acandidate itinerary ten days prior to departure. In each twoletter/number combination, the letter represents a booking class whilethe number represents the number of currently available seats that thedata source type (AVS or DCA) is showing.

EXAMPLE 1

Test date: Ten days prior to departure.

In AVS: Y7 B7 H7 Q7 K7 L4 N0 T0

In DCA: Y7 B7 H7 Q7 K5 L2 N0 T0

In Example 1, comparing the AVS and DCA data for class K reveals adifference of two seats, as does a comparison of available seats inclass L. If, for example, a customer requests three seats in L class,the AVS data would indicate that a candidate itinerary with three seatsis available for booking, while DCA data indicates that the flight isalready too full to accommodate the candidate itinerary. A differencewould also occur when AVS indicates that the client itinerary cannot besatisfied for lack of available seats, but DCA indicates that adequateseats have become available. If comparison of the AVS and DCA dataindicates that the AVS data does not differ from the DCA data, there isno need to update the data with DCA information (step 332). If, however,the AVS data is different than the DCA data, generally, when faced withthis candidate itinerary, the reservation system should obtain morecurrent availability information using DCA before booking the candidateitinerary. The situation table created at step 340 for that candidateitinerary therefore indicates that DCA information should be obtained.

In one embodiment of the present invention, even if a difference isdetected, DCA data will be used when an error threshold is exceeded. Inthis event, if a difference between AVS and DCA data is detected, it isfirst determined whether the difference exceeds an error threshold, suchas, for example, 98% (step 334). If the error threshold is exceeded,this candidate itinerary is annotated in the situation table as one whenDCA availability information should be obtained before booking (step338). If the error threshold is not exceeded, the situation table mayindicate that use of AVS data is sufficiently accurate (step 336).

In another embodiment of the present invention, the situation table willindicate that AVS data may be relied upon with sufficient accuracy andthat updating with DCA data is not warranted. For example, AVS data maybe used when the availability data forms a downward progression, thatis, the availability decreases as the class level decreases. Where thedata forms a downward progression, certain predictions can be reliablymade based on the AVS data. For example, if all the classes lower thanclass K have been closed or rendered irrelevant by the fare rules,generally class K will soon close as well and it is not necessary tocheck the data using DCA. These special situations may be reflected inthe situation table.

In step 350, the situation table is modified based on fare rules. Forexample, booking class L may require a 14-day advance purchase. InExample 1, however, the candidate itinerary has been requested only tendays in advance of departure. In this case, it does not matter that theDCA data may be more accurate because class L cannot be booked. In thiscase, the situation table will indicate that AVS data is sufficient.

In creating the situation tables, accounting for passenger impact isanother major consideration. An incorrect booking that involves fourpassengers, for example, has more overall effect than one that involvesonly a single passenger.

FIG. 4 is a flow diagram illustrating the steps of a method forincreasing reliability of booking airline travel itineraries based onnumber of passengers. The system begins by obtaining a candidateitinerary (step 400). In step 405, a reservation system implementing themethods of the present invention looks up the candidate itinerary in asituation table consistent with the present invention to determinewhether a situation similar to the candidate itinerary exists in thesituation table (step 410). If a situation similar to the candidateitinerary exists, the situation table will indicate whether theavailability information for this candidate itinerary should be updatedprior to booking. If the situation table indicates that DCA should beused (step 420), the availability information for this candidateitinerary is updated using DCA (step 425). If it indicates that updatingwill not increase reliability, the current flight availabilityinformation is used (step 430).

Modifications of this invention will occur to those skilled in the art.Therefore it is to be understood that this invention is not limited tothe particular method and system disclosed, but that it is intended tocover all modifications which are within the scope of this invention asclaimed.

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

1. A computer implemented method for predicting travel resourceavailability comprising the steps: receiving a candidate itinerary;obtaining current availability information for the candidate itinerary;determining a probability that the candidate itinerary will remainavailable for booking for a period of time in the future based at leastin part upon the current availability information and historicalavailability information for the candidate itinerary; and outputting theprobability.
 2. The method of claim 1, wherein the step of receiving acandidate itinerary further comprises: receiving a customer request fortravel; and selecting a candidate itinerary that satisfies the customerrequest.
 3. The method of claim 1, further comprising the step of:determining when the candidate itinerary will become unavailable forbooking based on fare rules.
 4. The method of claim 1, wherein the stepof determining a probability further comprises: determining when thecandidate itinerary will become unavailable given that a lower-priceditinerary has become unavailable.
 5. The method of claim 1, whereindetermining a probability further comprises: determining when thecandidate itinerary will become unavailable based upon a flightdeparture date.
 6. The method of claim 1, wherein determining aprobability further comprises: determining a probability that anunavailable itinerary will become available.
 7. A method for increasingreliability of booking airline travel itineraries comprising the stepsof: obtaining a candidate itinerary including availability information;and determining whether the availability information should be updatedbased on the candidate itinerary and a situation table that is createdbased upon availability data for the candidate itinerary from each of atleast two data sources, wherein the data sources comprise AvailabilityStatus (“AVS”) data and Direct Connect Availability (“DCA”) data.
 8. Themethod of claim 7 further comprising: creating the situation tablecomprising sample itineraries and historical availability information.9. The method of claim 7 further comprising: dynamically updating thesituation table based on the availability information.
 10. The method ofclaim 8, wherein creating a situation table comprises: obtainingavailability information from at least two data sources based on thecandidate itinerary; determining a difference between the availabilityinformation from the at least two sources; and storing in the situationtable an indication that the availability information should be updatedprior to booking, wherein the indication is based on the difference. 11.The method claim 10, wherein the storing step further comprises: storingin the situation table an indication that the availability informationshould be updated prior to booking but only when the candidate itineraryis not rendered irrelevant by fare rules.
 12. The method of claim 10,wherein the storing step further comprises: storing in the situationtable an indication that the availability information should be updatedprior to booking but only when a difference between the availabilityinformation from the at least two sources exceeds an error threshold.13. A system for predicting travel resource availability implemented ona computer, the system comprising: means for receiving a candidateitinerary; means for obtaining current availability information for thecandidate itinerary; means for determining a probability that thecandidate itinerary will remain available for booking for a period oftime in the future based at least in part upon the current availabilityinformation and historical availability information for the candidateitinerary; and means for outputting the probability.
 14. The system ofclaim 13 further comprising: means for receiving a customer request fortravel, and means for selecting a candidate itinerary that satisfies thecustomer request.
 15. The system of claim 14, further comprising: meansfor determining when the candidate itinerary will become unavailable forbooking based on fare rules.
 16. The system of claim 14, wherein themeans for determining a probability further comprises: means fordetermining when the candidate itinerary will become unavailable giventhat a lower-priced itinerary has become unavailable.
 17. The system ofclaim 14, wherein the means for determining a probability furthercomprises: means for determining when the candidate itinerary willbecome unavailable based upon a flight departure date.
 18. The system ofclaim 14, wherein the means for determining a probability furthercomprises: means for determining a probability that an unavailableitinerary will become available.
 19. A system for increasing reliabilityof booking airline travel itineraries implemented on a computer, thesystem comprising: means for obtaining a candidate itinerary includingavailability information; and means for determining whether theavailability information should be updated based on the candidateitinerary and a situation table that is created based upon availabilitydata for the candidate itinerary from each of at least two data sources,wherein the data sources comprise Availability Status (“AVS”) data andDirect Connect Availability (“DCA”) data.
 20. The system of claim 19further comprising: means for creating the situation table comprisingsample itineraries and historical availability information.
 21. Thesystem of claim 19 further comprising: means for dynamically updatingthe situation table based on the availability information.
 22. Thesystem of claim 20, wherein the means for creating a situation tablecomprises: means for obtaining availability information from at leasttwo data sources based on the candidate itinerary; means for determininga difference between the availability information from the at least twosources; and means for storing in the situation table an indication thatthe availability information should be updated prior to booking, whereinthe indication is based on the difference.
 23. The system of claim 22,wherein the means for storing further comprises: means for storing inthe situation table an indication that the availability informationshould be updated prior to booking but only when the candidate itineraryis not rendered irrelevant by fare rules.
 24. The system of claim 22,wherein the mean for storing further comprises: means for storing in thesituation table an indication that the availability information shouldbe updated prior to booking but only when a difference between theavailability information from the at least two sources exceeds an errorthreshold.
 25. A computer-readable medium containing instructions forcausing a computer to perform a method comprising the steps: receiving acandidate itinerary from an itinerary generation element; obtainingcurrent availability information for the candidate itinerary from anavailability data source; determining a probability that the candidateitinerary will remain available for booking for a period of time in thefuture based at least in part upon the current availability informationand historical availability information for the candidate itinerary by aprocessing element; and outputting the probability to a display element.26. The computer-readable medium of claim 25, wherein the step ofreceiving a candidate itinerary further comprises; receiving a customerrequest for travel; and selecting a candidate itinerary that satisfiesthe customer request.
 27. The computer-readable medium of claim 26,wherein the method further comprises the step of: determining when thecandidate itinerary will become unavailable for booking based on farerules.
 28. The computer-readable medium of claim 26, wherein the step ofdetermining a probability further comprises: determining when thecandidate itinerary will become unavailable given that a lower-priceditinerary has become unavailable.
 29. The computer-readable medium ofclaim 26, wherein determining a probability further comprises:determining when the candidate itinerary will become unavailable basedupon a flight departure date.
 30. The computer-readable medium of claim26, wherein determining a probability further comprises: determining aprobability that an unavailable itinerary will become available.
 31. Acomputer-readable medium containing instructions for causing a computerto perform a method of increasing reliability of booking airline travelitineraries comprising the steps of: obtaining a candidate itineraryincluding availability information from an itinerary generation elementin communication with an availability data source; and determiningwhether the availability information should be updated based on thecandidate itinerary and a situation table that is created based uponavailability data for the candidate itinerary from each of at least twodata sources by a processing element, wherein the data sources compriseAvailability Status (“AVS”) data and Direct Connect Availability (“DCA”)data.
 32. The computer-readable medium of claim 31, wherein the methodfurther comprises the step of: creating the situation table comprisingsample itineraries and historical availability information.
 33. Thecomputer-readable medium of claim 31, wherein the method furthercomprises the step of: dynamically updating the situation table based onthe availability information.
 34. The computer-readable medium of claim32, wherein the step of creating a situation table comprises the stepsof: obtaining availability information from at least two data sourcesbased on the candidate itinerary; determining a difference between theavailability information from the at least two sources; and storing inthe situation table an indication that the availability informationshould be updated prior to booking, wherein the indication is based onthe difference.
 35. The computer-readable medium of claim 34, whereinthe storing step further comprises: storing in the situation table anindication that the availability information should be updated prior tobooking but only when the candidate itinerary is not rendered irrelevantby fare rules.
 36. The computer-readable medium of claim 34, wherein thestoring step further comprises: storing in the situation table anindication that the availability information should be updated prior tobooking but only when a difference between the availability informationfrom the at least two sources exceeds an error threshold.